Towards Automated Metamorphic Test Identification for Ocean System Models

Metamorphic testing seeks to verify software in the absence of test oracles. Our application domain is ocean system modeling, where test oracles rarely exist, but where symmetries of the simulated physical systems are known. The input data set is large owing to the requirements of the application domain. This paper presents work in progress for the automated generation of metamorphic test scenarios using machine learning. We extended our previously proposed method [1] to identify metamorphic relations with reduced computational complexity. Initially, we represent metamorphic relations as identity maps. We construct a cost function that minimizes for identifying a metamorphic relation orthogonal to previously found metamorphic relations and penalize for the identity map. A machine learning algorithm is used to identify all possible metamorphic relations minimizing the defined cost function. We propose applying dimensionality reduction techniques to identify attributes in the input which have high variance among the identified metamorphic relations. We apply mutation on these selected attributes to identify distinct metamorphic relations with reduced computational complexity. For experimental evaluation, we subject the two implementations of an ocean-modeling application to the proposed method to present the use of metamorphic relations to test the two implementations of this application.Comment: 5 Pages, 1 Figur

Distributed T-Way Test Generation Strategies Using Tuple Space Approach

When generating a t-way (where t indicates the interaction strength) test suite for large and complex software systems, the number of interaction between software components to be covered for higher order t-way is likely to be huge and potentially leads towards a combinatorial explosion problem. Apart from being an NP complete problem, the computational complexity for t-way test suite generation also grows rapidly as the value of t increases. The resultant test case number in test suite also increases exponentially as the value of interaction strength, t is increases. In this manner, t-way test suite generation with large input parameter and high interaction strength require significantly high computational power and memory spaces. A myriad of useful t-way test suite generation strategies have been implemented recently using the sequential algorithm on standalone machines. Although helpful, the computational power and memory space of a standalone machine is arguably insufficient especially when dealing with large input parameters and high interaction strength. Furthermore, most of available strategies on t-way test suite generation cannot extend the computing work from standalone machines into a multiple machine environment